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This project is a comprehensive collection of common computer science algorithms and data structures implemented in Swift. It serves as an educational reference and library for studying computational complexity, algorithmic logic, and data structure engineering through practical code examples. The repository provides a wide suite of data structure implementations, including various types of linked lists, heaps, hash tables, and an extensive range of hierarchical trees such as Red-Black, B-Tree, and Splay trees. It also covers diverse sorting and searching techniques, from basic bubble sort to

LeetCode-Swift is a collection of algorithm solutions written in Swift, designed for coding interview preparation. Each solution is implemented as a self-contained function with no external dependencies, making it easy to run and test. The repository organizes solutions by topic and company, and every file includes time and space complexity annotations, allowing quick evaluation of algorithmic efficiency. What sets this repository apart is its flat file structure and the way solutions are tagged with the companies that asked them in interviews, enabling targeted practice. All code resides in

This project is a collection of comprehensive guides and reference materials designed for technical interviews, machine learning system design, and professional development. It serves as a technical knowledge base and a career coaching manual, providing structured resources to help candidates navigate the machine learning hiring landscape. The resource distinguishes itself by offering detailed frameworks for comparing industry roles, analyzing company types, and planning long-term career progression. It provides specific guidance on evaluating employer organizational health, identifying resea

This repository is a collection of fundamental computer science algorithms and data structures designed for educational and reference purposes. It provides a multi-language library of verified implementations for common computational patterns, serving as a resource for developers to study and apply standard logic to software engineering problems. The project distinguishes itself by offering modular, language-agnostic models that demonstrate how to implement essential storage patterns and classic algorithms. By providing implementations for tasks such as graph traversal, searching, and sorting

Dieses Repository dient als Bildungsressource für die Implementierung von Graph Neural Networks mit Python. Es bietet eine Sammlung strukturierter Codebeispiele und Tutorials, die Entwickler durch den Prozess des Aufbaus und Trainings von Machine-Learning-Modellen führen, die auf komplexen, miteinander verbundenen Datensätzen operieren. Das Projekt deckt die Kernmechaniken des graphbasierten Deep Learnings ab, einschließlich Message-Passing-Architekturen, Feature-Aggregation und dem Stapeln von Convolutional Layers. Es demonstriert, wie man nicht-euklidische Daten als statische Graphen darstellt und wie man den Speicher während des Trainings durch Mini-Batch-Sampling-Techniken verwaltet. Die enthaltenen Implementierungen adressieren gängige analytische Aufgaben wie Knotenklassifizierung, Graph-Strukturvorhersage und die Integration heterogener Datenquellen in einheitliche Modelle. Das Repository ist als eine Reihe praktischer Übungen organisiert, die theoretische Graph-Konzepte in funktionale Machine-Learning-Workflows übersetzen.